{"pageNumber":"247","pageRowStart":"6150","pageSize":"25","recordCount":46679,"records":[{"id":70210175,"text":"70210175 - 2020 - Fluoride occurrence in United States groundwater","interactions":[],"lastModifiedDate":"2020-05-19T13:38:58.950932","indexId":"70210175","displayToPublicDate":"2020-05-11T08:30:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Fluoride occurrence in United States groundwater","docAbstract":"Data from 38,105 wells were used to characterize fluoride (F) occurrence in untreated United States (U.S.) groundwater. For domestic wells (n = 11,032), water from which is generally not purposely fluoridated or monitored for quality, 10.9% of the samples have F concentrations >0.7 mg/L (U.S. Public Health Service recommended optimal F concentration in drinking water for preventing tooth decay) (87% are <0.7 mg/L); 2.6% have F > 2 mg/L (EPA Secondary Maximum Contaminant Level, SMCL); and 0.6% have F > 4 mg/L (EPA MCL). The data indicate the biggest concern with F in domestic wells at the national scale could be one of under consumption of F with respect to the oral-health benchmark (0.7 mg/L). Elevated F concentrations relative to the SMCL and MCL are regionally important, particularly in the western U.S. Statistical comparisons of potentially important controlling factors in four F-concentration categories (<0.1–0.7 mg/L; >0.7–2 mg/L; >2–4 mg/L; >4 mg/L) at the national scale indicate the highest F-concentration category is associated with groundwater that has significantly greater pH values, TDS and alkalinity concentrations, and well depths, and lower Ca/Na ratios and mean annual precipitation, than the lowest F-concentration category. The relative importance of the controlling factors appears to be regionally variable. Three case studies illustrate the spatial variability in controlling factors using groundwater-age (groundwater residence time), water-isotope (evaporative concentration), and water-temperature (geothermal processes) data. Populations potentially served by domestic wells with F concentrations <0.7, >0.7, >2, and >4 mg/L are estimated to be ~28,200,000, ~3,110,000; ~522,000; and ~172,000 people, respectively, in 40 principal aquifers with at least 25 F analyses per aquifer.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139217","collaboration":"","usgsCitation":"McMahon, P.B., Brown, C., Johnson, T., Belitz, K., and Lindsey, B.D., 2020, Fluoride occurrence in United States groundwater: Science of the Total Environment, v. 732, https://doi.org/10.1016/j.scitotenv.2020.139217.","productDescription":"139217, 15 p.","startPage":"","ipdsId":"IP-114693","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":456808,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139217","text":"Publisher Index Page"},{"id":436992,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CUPRIP","text":"USGS data release","linkHelpText":"Data for Fluoride Occurrence in United States Groundwater"},{"id":374917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n    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0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":213728,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":789430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Bruce D. 0000-0002-7180-4319 blindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-7180-4319","contributorId":175346,"corporation":false,"usgs":true,"family":"Lindsey","given":"Bruce","email":"blindsey@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":789431,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210389,"text":"70210389 - 2020 - Trends in thermal maturity indicators for the organic sulfur-rich Eagle Ford Shale","interactions":[],"lastModifiedDate":"2020-06-02T13:13:46.645331","indexId":"70210389","displayToPublicDate":"2020-05-11T08:10:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Trends in thermal maturity indicators for the organic sulfur-rich Eagle Ford Shale","docAbstract":"Thermal maturity is critical to evaluate petroleum systems and to interpret biomarker results for paleoenvironmental and geobiology studies. Many thermal maturity indices were developed for marine source rocks containing type II kerogen, but their behavior in organic sulfur-rich source rocks requires more investigation. Here, we present geochemical analyses of whole and extracted rock, isolated kerogens, and extractable organic matter across a natural thermal maturity transect of the Upper Cretaceous Eagle Ford Shale to evaluate the behavior of maturity parameters in organic sulfur-rich source rocks. The samples contain similar mineralogy and trace element composition, minimizing potential facies effects on thermal maturity parameters. Atomic H/C ratios of isolated kerogens, extractable organic matter yield, and programmed pyrolysis results show that the samples range from the pre-oil through dry gas generation windows. Programmed pyrolysis data and kerogen elemental ratios show that the immature samples host both type IIS (atomic Sorg/C > 0.04) and sulfur-rich type II kerogen (kerogen Sorg/C: 0.032 to 0.045) while the samples with lower kerogen Sorg/C ratios (kerogen Sorg/C < 0.03) are more mature. The vitrinite reflectance values corresponding to the onset of oil generation in the Eagle Ford are comparable to other type II petroleum systems. Likewise, the organic sulfur-rich Eagle Ford follows a similar hydrogen index and kerogen H/C trend as other marine type II source rocks. Hopane and sterane stereoisomer maturity ratios are anomalously elevated at low thermal maturities, so they should not be applied in organic sulfur-rich petroleum systems to infer thermal maturity. However, some biomarker ratios, including those that are more commonly used as source parameters, strongly track thermal maturity in this case study with limited facies variability.","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2020.104459","usgsCitation":"French, K.L., Birdwell, J.E., and Lewan, M., 2020, Trends in thermal maturity indicators for the organic sulfur-rich Eagle Ford Shale: Marine and Petroleum Geology, v. 118, 104459, 21 p., https://doi.org/10.1016/j.marpetgeo.2020.104459.","productDescription":"104459, 21 p.","ipdsId":"IP-116987","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":456812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2020.104459","text":"Publisher Index Page"},{"id":375242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"118","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":790138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":790137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lewan, Michael 0000-0001-6347-1553 mlewan@usgs.gov","orcid":"https://orcid.org/0000-0001-6347-1553","contributorId":173938,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael","email":"mlewan@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":790139,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70210015,"text":"ofr20201049 - 2020 - 2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California","interactions":[],"lastModifiedDate":"2020-05-11T11:55:34.993752","indexId":"ofr20201049","displayToPublicDate":"2020-05-08T15:09:35","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1049","displayTitle":"2018 U.S. Geological Survey–California Geological Survey Fault-Imaging Surveys Across the Hollywood and Santa Monica Faults, Los Angeles County, California","title":"2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California","docAbstract":"<p>We acquired multiple types of seismic data across the Hollywood Fault in Hollywood, Calif., and the Santa Monica Fault in Beverly Hills, Calif., in May and June 2018. On the basis of our data, we infer near-surface locations of various traces of these faults.<br>From two separate profiles across the Hollywood Fault, we evaluated multiple seismic datasets and models, including guided-wave data, tomographic V<sub>P</sub> data, tomographic V<sub>S</sub> data, V<sub>P</sub>/V<sub>S</sub> and Poisson’s ratio models derived from tomographic V<sub>P</sub> and V<sub>S</sub> data, Rayleigh-wave–based V<sub>S</sub> models, Love-wave–based V<sub>S</sub> models, V<sub>P</sub>/V<sub>S</sub> and Poisson’s ratio models (derived from combinations of tomographic-based V<sub>P</sub> and surface-wave–based V<sub>S</sub> models), P-wave reflection images, and S-wave reflection images. All of these data and models can be used to delineate near-surface faulting, and the data consistently infer near-surface fault traces of the Hollywood Fault in the same locations. Importantly, the combined data indicate more than one near-surface fault trace of the Hollywood Fault. Between North Bronson and North Gower Avenues, evidence exists for a near-surface trace of the Hollywood Fault slightly south of Carlos Avenue. Farther west, along Argyle Avenue, our data contain high levels of cultural noise, but we interpret near-surface faulting slightly south of the intersection of Carlos and Argyle Avenues and between Carlos Avenue and Yucca Street.<br>For the Santa Monica Fault in Beverly Hills, we acquired guided-wave data only along Lasky Drive between Moreno Drive and South Santa Monica Boulevard, owing to limited access permissions. However, we used two separate source locations to generate the guided-wave data (SP1 and SP2). The data from more distant source location (relative to the recording array, SP1) were noisy, but on the basis of those data, we infer near-surface faulting at several locations along Lasky Drive, with concentrated near-surface faulting slightly south of the intersection of Lasky Drive and Charleville Boulevard. Guided-wave data generated at the closer source location (relative to recording array, SP2) more clearly show evidence for distributed near-surface faulting at several locations along Lasky Drive, with concentrated faulting near the intersection of Lasky Drive and Charleville Boulevard.<br>Although the seismic surveys across both faults provide strong evidence for the locations of near-surface fault traces, the seismic data provide little or no information about the rupture history of the fault traces.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201049","collaboration":"Prepared in cooperation with California Geological Survey","usgsCitation":"Catchings, R.D., Hernandez, J., Goldman, M.R., Chan, J.H., Sickler, R.R., Olson, B., and Criley, C.J., 2020, 2018 U.S. Geological Survey–California Geological Survey fault-imaging surveys across the Hollywood and Santa Monica Faults, Los Angeles County, California: U.S. Geological Survey Open-File Report 2020–1049, 42 p., https://doi.org/10.3133/ofr20201049.","productDescription":"Report: vi, 42 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-113953","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374593,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENA8D4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for the 2018 U.S. Geological Survey–California Geological Survey Fault-Imaging Surveys Across the Hollywood and Santa Monica Faults, Los Angeles County, California"},{"id":374591,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1049/coverthb.jpg"},{"id":374592,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1049/ofr20201049.pdf","text":"Report","size":"15.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1049"}],"country":"United States","state":"California ","county":"Los Angeles County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.49304199218749,\n              33.80653802509606\n            ],\n            [\n              -117.81875610351562,\n              33.529947711130646\n            ],\n            [\n              -117.476806640625,\n              33.742612777346864\n            ],\n            [\n              -117.52624511718749,\n              34.47712785074854\n            ],\n            [\n              -118.60290527343749,\n              34.45674800347809\n            ],\n            [\n              -118.83911132812499,\n              34.098159345215535\n            ],\n            [\n              -118.49304199218749,\n              33.80653802509606\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"https://www.usgs.gov/natural-hazards/earthquake-hazards/connect\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/natural-hazards/earthquake-hazards/connect\">Contact Information, Menlo Park, Calif.</a><br><a href=\"https://earthquake.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://earthquake.usgs.gov/\">Office—Earthquake Science Center</a><br>U.S. Geological Survey<br>345 Middlefield Road, MS 977<br>Menlo Park, CA 94025</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Seismic Methodologies</li><li>Hollywood Fault Data Acquisition and Profiles</li><li>Guided-Waves Results for Profile HW1</li><li>Tomography, MASW, and Reflection Results for Profile HW1</li><li>Summary of Seismic Indicators of Faulting along Profile HW1</li><li>Guided-Wave Results for Profile HW2</li><li>Tomography, MASW, and Reflection Results for Profile HW2</li><li>Summary of Seismic Indicators of Faulting along Profile HW2</li><li>Summary of Observations, Hollywood Fault</li><li>Santa Monica Fault Data Acquisition (Beverly Hills)</li><li>Santa Monica Fault Data Analysis</li><li>Summary of Observations, Santa Monica Fault, Beverly Hills</li><li>References Cited</li></ul>","publishedDate":"2020-05-08","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hernandez, Janis","contributorId":216335,"corporation":false,"usgs":false,"family":"Hernandez","given":"Janis","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":788806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldman, Mark R. 0000-0002-0802-829X goldman@usgs.gov","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":1521,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","email":"goldman@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chan, Joanne H. 0000-0002-2065-2423 jchan@usgs.gov","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":178625,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","email":"jchan@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sickler, Robert R. 0000-0002-9141-625X rsickler@usgs.gov","orcid":"https://orcid.org/0000-0002-9141-625X","contributorId":3235,"corporation":false,"usgs":true,"family":"Sickler","given":"Robert","email":"rsickler@usgs.gov","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Olson, Brian","contributorId":217365,"corporation":false,"usgs":false,"family":"Olson","given":"Brian","affiliations":[{"id":12640,"text":"California Geological Survey","active":true,"usgs":false}],"preferred":false,"id":788810,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Criley, Coyn J. 0000-0002-0227-0165 ccriley@usgs.gov","orcid":"https://orcid.org/0000-0002-0227-0165","contributorId":3312,"corporation":false,"usgs":true,"family":"Criley","given":"Coyn","email":"ccriley@usgs.gov","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":788811,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70209808,"text":"sir20205037 - 2020 - Compositional analysis of formation water geochemistry and microbiology of commercial and carbon dioxide-rich wells in the southwestern United States","interactions":[],"lastModifiedDate":"2020-05-11T11:42:40.648542","indexId":"sir20205037","displayToPublicDate":"2020-05-08T14:55:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5037","displayTitle":"Compositional Analysis of Formation Water Geochemistry and Microbiology of Commercial and Carbon Dioxide-Rich Wells in the Southwestern United States","title":"Compositional analysis of formation water geochemistry and microbiology of commercial and carbon dioxide-rich wells in the southwestern United States","docAbstract":"<p>Studies of naturally occurring subsurface carbon dioxide (CO<sub>2</sub>) accumulations can provide useful information for potential CO<sub>2</sub> injection projects; however, the microbial communities and formation water geochemistry of most reservoirs are understudied. Formation water and microbial biomass were sampled at four CO<sub>2</sub>-rich reservoir sites: two within Bravo Dome, a commercial CO<sub>2</sub> field in New Mexico; one northwest of Bravo Dome in Colorado (Oakdale Field); and one southwest of Bravo Dome in New Mexico (Rafter “K” Ranch). Aside from the Rafter “K” Ranch site, minor differences were observed in the geochemistry of formation water collected from these sites compared to historical data. No organisms were significantly associated with Oakdale Field compared to the other three sites, nor were any hydrogeochemical or gas geochemical parameters (for example, CO<sub>2</sub> concentration) found to have significant associations with the microbial ecology of these four sites. Microorganisms from these sites were metabolically diverse and had the potential to (1) generate methane, (2) produce corrosive hydrogen sulfide (H<sub>2</sub>S), and (3) rapidly biofoul and (or) clog pore spaces by shifting microbial communities with changes in salinity or nutrient supply. This study demonstrates that high concentrations of CO<sub>2</sub> in subsurface reservoirs apparently have not imparted a distinct geochemical or microbiological signature on the associated formation waters and that the microorganisms in these reservoirs are metabolically diverse and could adapt to geochemical changes in the subsurface.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205037","usgsCitation":"Shelton, J.L., Andrews, R.S., Akob, D.M., DeVera, C.A., Mumford, A.C., Engle, M., Plampin, M.R., and Brennan, S.T., 2020, Compositional analysis of formation water geochemistry and microbiology of commercial and carbon dioxide-rich wells in the southwestern United States: U.S. Geological Survey Scientific Investigations Report 2020–5037, 26 p., https://doi.org/10.3133/sir20205037.","productDescription":"viii, 26 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098514","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":374365,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5037/sir20205037.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5037"},{"id":374364,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5037/coverthb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Texas, Oklahoma","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.16064453125,\n              34.07086232376631\n            ],\n            [\n              -102.919921875,\n              34.07086232376631\n            ],\n            [\n              -102.919921875,\n              37.43997405227057\n            ],\n            [\n              -107.16064453125,\n              37.43997405227057\n            ],\n            [\n              -107.16064453125,\n              34.07086232376631\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/eersc\" data-mce-href=\"https://www.usgs.gov/centers/eersc\">Eastern Energy Resources Science Center</a><br>U.S. Geological Survey<br>12201 Sunrise Valley Drive<br>956 National Center<br>Reston, VA 20192</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Background and Study Sites</li><li>Methods</li><li>Produced Fluid Geochemistry</li><li>Microbial Community Composition and Diversity</li><li>Growth and Activity of Microbial Functional Groups in the Rafter “K” Ranch and Oakdale Field Samples</li><li>Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-05-08","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":788114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrews, Robert S. 0000-0002-6166-720X","orcid":"https://orcid.org/0000-0002-6166-720X","contributorId":204981,"corporation":false,"usgs":true,"family":"Andrews","given":"Robert","email":"","middleInitial":"S.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":788115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Akob, Denise M. 0000-0003-1534-3025 dakob@usgs.gov","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":4980,"corporation":false,"usgs":true,"family":"Akob","given":"Denise","email":"dakob@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true}],"preferred":true,"id":788116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeVera, Christina A. 0000-0002-4691-6108 cdevera@usgs.gov","orcid":"https://orcid.org/0000-0002-4691-6108","contributorId":3845,"corporation":false,"usgs":true,"family":"DeVera","given":"Christina","email":"cdevera@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":788117,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mumford, Adam C. 0000-0002-8082-8910 amumford@usgs.gov","orcid":"https://orcid.org/0000-0002-8082-8910","contributorId":197795,"corporation":false,"usgs":true,"family":"Mumford","given":"Adam","email":"amumford@usgs.gov","middleInitial":"C.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":788118,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":222085,"corporation":false,"usgs":true,"family":"Engle","given":"Mark","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":788119,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Plampin, Michelle R. 0000-0003-4068-5801 mplampin@usgs.gov","orcid":"https://orcid.org/0000-0003-4068-5801","contributorId":204983,"corporation":false,"usgs":true,"family":"Plampin","given":"Michelle","email":"mplampin@usgs.gov","middleInitial":"R.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":788120,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":788121,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70214538,"text":"70214538 - 2020 - Applications and utility of the surface elevation table–marker horizon method for measuring wetland elevation and shallow soil subsidence-expansion: Discussion/reply to: Byrnes M., Britsch L., Berlinghoff J., Johnson R., and Khalil S. 2019. Recent subsidence rates for Barataria Basin, Louisiana. Geo-Marine Letters 39:265–278","interactions":[],"lastModifiedDate":"2020-09-30T14:49:02.792091","indexId":"70214538","displayToPublicDate":"2020-05-08T09:47:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1742,"text":"Geo-Marine Letters","active":true,"publicationSubtype":{"id":10}},"title":"Applications and utility of the surface elevation table–marker horizon method for measuring wetland elevation and shallow soil subsidence-expansion: Discussion/reply to: Byrnes M., Britsch L., Berlinghoff J., Johnson R., and Khalil S. 2019. Recent subsidence rates for Barataria Basin, Louisiana. Geo-Marine Letters 39:265–278","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Byrnes et al. (Geo-Marine Letters 39:265–278, Byrnes et al.<span>&nbsp;</span><a id=\"ref-link-section-d16467e434\" title=\"Byrnes MR, Britsch LD, Berlinghoff JL, Johnson R, Khalil S (2019) Recent subsidence rates for Barataria Basin, Louisiana. Geo-Mar Lett 39:265–278\" href=\"https://link.springer.com/article/10.1007/s00367-020-00656-6#ref-CR2\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2019\" data-mce-href=\"https://link.springer.com/article/10.1007/s00367-020-00656-6#ref-CR2\">2019</a>) present subsidence data for Barataria Basin located south and west of New Orleans in coastal Louisiana to better inform wetland protection and restoration planning by the Louisiana Coastal Protection and Restoration Authority. They measured subsidence using geodetic GPS elevation surveys of rod benchmarks, similar to the rod benchmarks of the surface elevation table–marker horizon (SET-MH) method used to measure surface biophysical processes influencing elevation dynamics and shallow subsidence (i.e., subsidence occurring above the base of the rod) in coastal wetlands. Byrnes et al. (Geo-Marine Letters 39:265–278, Byrnes et al.<span>&nbsp;</span><a id=\"ref-link-section-d16467e437\" title=\"Byrnes MR, Britsch LD, Berlinghoff JL, Johnson R, Khalil S (2019) Recent subsidence rates for Barataria Basin, Louisiana. Geo-Mar Lett 39:265–278\" href=\"https://link.springer.com/article/10.1007/s00367-020-00656-6#ref-CR2\" data-track=\"click\" data-track-action=\"reference anchor\" data-track-label=\"link\" data-test=\"citation-ref\" aria-label=\"Reference 2019\" data-mce-href=\"https://link.springer.com/article/10.1007/s00367-020-00656-6#ref-CR2\">2019</a>) argue that (1) SET-MH measures should not be included in subsidence measures because subsidence is a purely geologic process, separate from biophysical processes occurring in the active marsh zone, (2) shallow subsidence measured by the SET-MH method in deep Holocene sediments are not valid because of downdrag on the rod, and (3) high spatial variability of wetland surface processes precludes the ability to make meaningful estimates of subsidence using the SET-MH method. This reply paper presents an extensive summary of the peer-reviewed literature that refutes all three of these claims and demonstrates that it is not only reasonable but also essential to apply the SET-MH method to obtain a complete as possible assessment of surface elevation dynamics to inform coastal wetland restoration and management planning in Barataria Basin and other coastal wetlands worldwide.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s00367-020-00656-6","usgsCitation":"Cahoon, D., Reed, D., Day, J.W., Lynch, J.C., Swales, A., and Lane, R.R., 2020, Applications and utility of the surface elevation table–marker horizon method for measuring wetland elevation and shallow soil subsidence-expansion: Discussion/reply to: Byrnes M., Britsch L., Berlinghoff J., Johnson R., and Khalil S. 2019. Recent subsidence rates for Barataria Basin, Louisiana. Geo-Marine Letters 39:265–278: Geo-Marine Letters, v. 40, p. 809-815, https://doi.org/10.1007/s00367-020-00656-6.","productDescription":"7 p.","startPage":"809","endPage":"815","ipdsId":"IP-115288","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":378911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Cahoon, Donald R. 0000-0002-2591-5667","orcid":"https://orcid.org/0000-0002-2591-5667","contributorId":219657,"corporation":false,"usgs":true,"family":"Cahoon","given":"Donald","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":799844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":799845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, John W.","contributorId":200323,"corporation":false,"usgs":false,"family":"Day","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":799846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynch, James C.","contributorId":179352,"corporation":false,"usgs":false,"family":"Lynch","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":799847,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swales, Andrew","contributorId":149632,"corporation":false,"usgs":false,"family":"Swales","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":799848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lane, Robert R.","contributorId":195573,"corporation":false,"usgs":false,"family":"Lane","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":16756,"text":"Louisiana State University, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":799849,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70210688,"text":"70210688 - 2020 - Projected impacts of climate change on the range and phenology of three culturally-important shrub species","interactions":[],"lastModifiedDate":"2020-06-17T13:34:51.108521","indexId":"70210688","displayToPublicDate":"2020-05-08T08:26:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Projected impacts of climate change on the range and phenology of three culturally-important shrub species","docAbstract":"<p><span>Climate change is shifting both the habitat suitability and the timing of critical biological events, such as flowering and fruiting, for plant species across the globe. Here, we ask how both the distribution and phenology of three food-producing shrubs native to northwestern North America might shift as the climate changes. To address this question, we compared gridded climate data with species location data to identify climate variables that best predicted the current bioclimatic niches of beaked hazelnut (</span><i>Corylus cornuta)</i><span>, Oregon grape (</span><i>Mahonia aquifolium</i><span>), and salal (</span><i>Gaultheria shallon</i><span>). We also developed thermal-sum models for the timing of flowering and fruit ripening for these species. We then used multi-model ensemble future climate projections to estimate how species range and phenology may change under future conditions. Modelling efforts showed extreme minimum temperature, climate moisture deficit, and mean summer precipitation were predictive of climatic suitability across all three species. Future bioclimatic niche models project substantial reductions in habitat suitability across the lower elevation and southern portions of the species’ current ranges by the end of the 21</span><sup>st</sup><span>&nbsp;century. Thermal-sum phenology models for these species indicate that flowering and the ripening of fruits and nuts will advance an average of 25 days by the mid-21</span><sup>st</sup><span>&nbsp;century, and 36 days by the late-21</span><sup>st</sup><span>&nbsp;century under a high emissions scenario (RCP 8.5). Future changes in the climatic niche and phenology of these important food-producing species may alter trophic relationships, with cascading impacts on regional ecosystems.</span></p>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0232537","usgsCitation":"Prevey, J.S., Parker, L.E., and Harrington, C., 2020, Projected impacts of climate change on the range and phenology of three culturally-important shrub species: PLoS ONE, v. 15, no. 5, e0232537, 19 p., https://doi.org/10.1371/journal.pone.0232537.","productDescription":"e0232537, 19 p.","ipdsId":"IP-114286","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456827,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0232537","text":"Publisher Index Page"},{"id":436996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G0UTKF","text":"USGS data release","linkHelpText":"Location and phenology observations for beaked hazelnut (Corylus cornuta), Oregon grape (Mahonia aquifolium), and salal (Gaultheria shallon) in western North America"},{"id":375664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British, Columbia, California, Idaho, Montana, Nevada, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.4560546875,\n              47.57652571374621\n            ],\n            [\n              -117.333984375,\n              50.56928286558243\n            ],\n            [\n              -121.5087890625,\n              52.32191088594773\n            ],\n            [\n              -133.2421875,\n              54.34214886448341\n            ],\n            [\n              -132.7587890625,\n              52.9883372533954\n            ],\n            [\n              -126.60644531250001,\n              48.922499263758255\n            ],\n            [\n              -124.541015625,\n              46.07323062540835\n            ],\n            [\n              -125.0244140625,\n              42.22851735620852\n            ],\n            [\n              -125.068359375,\n              39.80853604144591\n            ],\n            [\n              -120.4541015625,\n              33.797408767572485\n            ],\n            [\n              -117.333984375,\n              35.92464453144099\n            ],\n            [\n              -120.32226562500001,\n              40.01078714046552\n            ],\n            [\n              -119.00390625,\n              40.91351257612758\n            ],\n            [\n              -112.32421875,\n              42.52069952914966\n            ],\n            [\n              -111.357421875,\n              45.61403741135093\n            ],\n            [\n              -111.8408203125,\n              47.100044694025215\n            ],\n            [\n              -112.4560546875,\n              47.57652571374621\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Prevey, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevey","given":"Janet","email":"","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":790978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Lauren E.","contributorId":225389,"corporation":false,"usgs":false,"family":"Parker","given":"Lauren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":790979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrington, Constance A","contributorId":167297,"corporation":false,"usgs":false,"family":"Harrington","given":"Constance A","affiliations":[{"id":24677,"text":"USDA  Pacific Northwest Research Station, Olympia WA","active":true,"usgs":false}],"preferred":false,"id":790980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70215238,"text":"70215238 - 2020 - GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning","interactions":[],"lastModifiedDate":"2020-10-14T12:34:15.624122","indexId":"70215238","displayToPublicDate":"2020-05-08T07:33:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3618,"text":"Transactions in GIS","active":true,"publicationSubtype":{"id":10}},"title":"GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning","docAbstract":"<div id=\"article__content\" class=\"col-sm-12 col-md-8 col-lg-8 article__content article-row-left\"><div class=\"article__body \"><div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.</p></div></div></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/tgis.12633","usgsCitation":"Arundel, S., Li, W., and Wang, S., 2020, GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning: Transactions in GIS, v. 24, no. 3, p. 556-572, https://doi.org/10.1111/tgis.12633.","productDescription":"17 p.","startPage":"556","endPage":"572","ipdsId":"IP-115822","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":436998,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X5BN1L","text":"USGS data release","linkHelpText":"GeoNatShapes: a natural feature reference dataset for mapping and AI training"},{"id":379346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":801249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Wenwen 0000-0003-2237-9499","orcid":"https://orcid.org/0000-0003-2237-9499","contributorId":219356,"corporation":false,"usgs":false,"family":"Li","given":"Wenwen","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":801250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Sizhe","contributorId":242975,"corporation":false,"usgs":false,"family":"Wang","given":"Sizhe","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":801251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70209795,"text":"sir20205043 - 2020 - Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015","interactions":[],"lastModifiedDate":"2020-05-07T19:58:50.668535","indexId":"sir20205043","displayToPublicDate":"2020-05-07T10:17:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5043","displayTitle":"Chemical Evaluation of Water and Gases Collected from Hydrothermal Systems Located in the Central Aleutian Arc, August 2015","title":"Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015","docAbstract":"<p>Five volcanic-hydrothermal systems in the central Aleutians Islands were sampled for water and gas geochemistry in 2015 to provide baseline data to help predict future volcanic unrest. Some areas had not been sampled in 20–30 years (Makushin volcano, Geyser Bight), and other areas had minimal to no prior sampling (Tana volcano and Fisher Caldera). The chemical and isotopic data of the waters show a wide variety of characteristics typical of hydrothermal settings. Stable isotopic analyses of the waters show no evidence for primary magmatic water, rather that waters have a meteoric origin that is variably influenced by boiling and evaporation processes. The carbon and helium isotopic analyses of gases suggest they contain a primary magmatic component typical of the upper mantle at most locations, and the CO<sub>2</sub>/S ratios show that these gases have been modified by interactions with groundwater along the flow paths. Some areas demonstrate stable compositions since the last sampling (for example, Akutan hydrothermal areas), with some being remarkably steady over very long periods (for example, Geyser Bight). Other areas show modifications because of either lower amounts of upwelling from hydrothermal sources or lower amounts of magmatic influence on the surface chemistry (for example, Upper Glacial valley of Makushin, an informally named valley leading south of the volcano toward Makushin Bay to the south). Finally, this report highlights that previously unsampled regions in the Aleutian Islands, such as Tana volcano and Fisher Caldera (the latter found to have one of the highest helium isotopic signatures ever measured in the Aleutian Islands), show evidence of ongoing subsurface magmatism that warrants continued investigation in terms of volcanic hazard.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205043","collaboration":"","usgsCitation":"Werner, C., Kern, C., and Kelly, P. K., 2020, Chemical evaluation of water and gases collected from hydrothermal systems located in the central Aleutian arc, August 2015: U.S. Geological Survey Scientific Investigations Report 2020–5043, 35 p., https://doi.org/10.3133/sir20205043.","productDescription":"Report: viii, 35 p.; 2 Tables","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118716","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":374537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5043/coverthb.jpg"},{"id":374538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374539,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table1.pdf","text":"Table 1","size":"200 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":374540,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5043/sir20205043_table2.pdf","text":"Table 2","size":"130 KB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","county":"","city":"","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -169.2333984375,\n              52.81604319154934\n            ],\n            [\n              -168.96972656249997,\n              52.669720383688166\n            ],\n            [\n              -168.4423828125,\n              52.8691297276852\n            ],\n            [\n              -168.28857421875,\n              53.08082737207479\n            ],\n            [\n              -167.62939453124997,\n              53.1335898292448\n            ],\n            [\n              -166.75048828125,\n              53.30462107510271\n            ],\n            [\n              -166.13525390625,\n              53.657661020298\n            ],\n            [\n              -164.99267578125,\n              54.00776876193478\n            ],\n            [\n              -164.11376953125,\n              54.23955053156177\n            ],\n            [\n              -164.33349609375,\n              54.67383096593114\n            ],\n            [\n              -164.68505859375,\n              54.88924640307589\n            ],\n            [\n              -165.41015625,\n              54.648412502316695\n            ],\n            [\n              -166.57470703125,\n              54.316523240258256\n            ],\n            [\n              -168.77197265625,\n              53.605544099238\n            ],\n            [\n              -169.2333984375,\n              52.81604319154934\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:tlmurray@usgs.gov\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"mailto:tlmurray@usgs.gov\">Director</a>,<br><a href=\"https://volcanoes.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://volcanoes.usgs.gov/\">Volcano Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov/\">U.S. Geological Survey</a><br>4210 University Drive<br>Anchorage, AK 99508</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Makushin Volcano</li><li>Akutan Volcano</li><li>Tana Volcano</li><li>Fisher Caldera</li><li>Geyser Bight Hydrothermal Area</li><li>Conclusions</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2020-04-24","noUsgsAuthors":false,"publicationDate":"2020-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Werner, Cynthia A. cwerner@usgs.gov","contributorId":2540,"corporation":false,"usgs":true,"family":"Werner","given":"Cynthia","email":"cwerner@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":788058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":788059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":788060,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70216725,"text":"70216725 - 2020 - History and evolution of seepage meters for quantifying flow between groundwater and surface water: Part 1 – Freshwater settings","interactions":[],"lastModifiedDate":"2020-12-03T12:48:45.492738","indexId":"70216725","displayToPublicDate":"2020-05-06T17:01:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"History and evolution of seepage meters for quantifying flow between groundwater and surface water: Part 1 – Freshwater settings","docAbstract":"More than 75 years after its introduction, the seepage meter remains the only device for directly quantifying exchange across the sediment-water interface between groundwater and surface water.  This device, first presented in the literature in the 1940s, has been in a state of near-constant improvement and design change, necessitating a review of the history and evolution of the device and a description of current best-measurement practices.  Part 1 of this two-part review documents the evolution of seepage meters deployed in freshwater settings, including a listing of suggestions for best-measurement and deployment practices.  Part 2 covers the same scope for seepage meters deployed in marine settings.  Traditional seepage meters isolate a portion of the sediment bed; seepage commonly is determined by routing the volume of flow across that isolated interface to or from a submerged measurement bag over a known time interval.  The time-integrated volume is then divided by the bed area covered by the meter to obtain a seepage flux expressed in distance per time.  Both the instrument and the measurement are deceptively simple, leading some early users to question the viability of the measurement.  Numerous sources of error have been identified and addressed over the decades, resulting in large improvements in measurement consistency and accuracy.  Duration of each measurement depends on the seepage rate and can vary from minutes to days, leading to the erroneous and yet common assumption that seepage is relatively stable over time.  Designs that replace the measurement bag with a flowmeter eliminate bag-related errors and provide much finer temporal resolution.  Resulting data indicate seepage is highly variable in many settings and responds to numerous sub-daily processes, including evapotranspiration, rainfall, seiches and waves.  Combining direct measurements from seepage meters with other measurements, such as vertical hydraulic gradients and vertical temperature profiles, provides far better understanding of the processes that control exchange between groundwater and surface water.","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2020.103167","usgsCitation":"Rosenberry, D.O., Duque, C., and Lee, D.R., 2020, History and evolution of seepage meters for quantifying flow between groundwater and surface water: Part 1 – Freshwater settings: Earth-Science Reviews, v. 204, 103167, 13 p., https://doi.org/10.1016/j.earscirev.2020.103167.","productDescription":"103167, 13 p.","ipdsId":"IP-113122","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":380934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"204","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":805993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duque, Carlos 0000-0001-5833-8483","orcid":"https://orcid.org/0000-0001-5833-8483","contributorId":245349,"corporation":false,"usgs":false,"family":"Duque","given":"Carlos","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":805994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, David R.","contributorId":176828,"corporation":false,"usgs":false,"family":"Lee","given":"David","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":805995,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70211851,"text":"70211851 - 2020 - Geophysical characterization of the Northwest Geysers geothermal field, California","interactions":[],"lastModifiedDate":"2020-08-11T13:01:58.184934","indexId":"70211851","displayToPublicDate":"2020-05-06T09:26:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical characterization of the Northwest Geysers geothermal field, California","docAbstract":"The Clear Lake Volcanic Field in northern California is the youngest and northern-most part of a long-lived volcanic system that has produced recent (~10 ka) eruptions.  Adjacent to the Clear Lake Volcanic Field is the worlds largest energy producing geothermal field, The Geysers.  The hottest part of The Geysers geothermal field is in the northwest where temperatures reach ~400 C at 3 km depth. Low permeability, high thermal gradients, and low steam saturation prescribed development of an enhanced geothermal system (EGS) in the Northwest Geysers to increase energy producing capacity. Though the Northwest Geysers is known to be the hottest part of the field, geophysical methods have failed to adequately image any inferred heat source. This project aims to image the heat source of the Northwest Geysers using newly collected gravity and magnetotelluric (MT) measurements.  Gravity data were jointly modeled with existing magnetic data along a two-dimensional profile aligned with an existing geologic cross-section. The key feature of the potential field model is a low-density, low-susceptibility body at 5 km depth (bmsl) under the EGS.  MT data were modeled in three-dimensions to characterize subsurface resistivity structure, where the upper 3 km of the resistivity model agrees well with existing data.  Lithologic and steam saturation are estimated from modeled resistivity values using existing geophysical data.  Below 3 km depth (bmsl), the resistivity model images a possible young intrusion under the EGS. A possible zone of partial melt (<5%) below 7 km depth (bmsl) in the northwestern part of the field is also imaged which extends northeast towards the main part of the Clear Lake Volcanic Field.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106882","usgsCitation":"Peacock, J., Earney, T.E., Mangan, M.T., Schermerhorn, W.D., Glen, J.M., Walters, M., and Hartline, C., 2020, Geophysical characterization of the Northwest Geysers geothermal field, California: Journal of Volcanology and Geothermal Research, v. 339, 106882, 17 p., https://doi.org/10.1016/j.jvolgeores.2020.106882.","productDescription":"106882, 17 p.","ipdsId":"IP-117752","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Twin Lakes, Glenview","otherGeospatial":"Southern Clear Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.80105590820314,\n              38.85682013474361\n            ],\n            [\n              -122.53875732421875,\n              38.85682013474361\n            ],\n            [\n              -122.53875732421875,\n              38.94338908847991\n            ],\n            [\n              -122.80105590820314,\n              38.94338908847991\n            ],\n            [\n              -122.80105590820314,\n              38.85682013474361\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"339","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mangan, Margret T. 0000-0002-5273-8053","orcid":"https://orcid.org/0000-0002-5273-8053","contributorId":237813,"corporation":false,"usgs":false,"family":"Mangan","given":"Margret","email":"","middleInitial":"T.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":795393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Mark 0000-0001-8458-4813","orcid":"https://orcid.org/0000-0001-8458-4813","contributorId":213428,"corporation":false,"usgs":false,"family":"Walters","given":"Mark","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":795397,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":795396,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70211980,"text":"70211980 - 2020 - Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.","interactions":[],"lastModifiedDate":"2020-08-12T23:12:31.627153","indexId":"70211980","displayToPublicDate":"2020-05-05T18:02:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.","docAbstract":"<p><span>Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km</span><sup>2</sup><span>&nbsp;year</span><sup>-1</sup><span>) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km</span><sup>2</sup><span>&nbsp;over the four-year period, and 186 km</span><sup>2</sup><span>, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs12091464","usgsCitation":"Vanderhoof, M.K., Christensen, J.R., Beal, Y.G., DeVries, B., Lang, M.W., Hwang, N., Mazzarella, C., and Jones, J., 2020, Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.: Remote Sensing, v. 12, no. 9, 1464, 29 p., https://doi.org/10.3390/rs12091464.","productDescription":"1464, 29 p.","ipdsId":"IP-116446","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35993,"text":"Hydrologic Investigations and Research Section","active":true,"usgs":true}],"links":[{"id":456841,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091464","text":"Publisher Index Page"},{"id":437000,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ODILGN","text":"USGS data release","linkHelpText":"Tracking disturbance and inundation to identify wetland loss"},{"id":377459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, MarylandPennsylvania, Virginia, West Virginia","otherGeospatial":"Mid-Atlantic Region","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.70703125,\n              41.44272637767212\n            ],\n            [\n              -75.05859375,\n              41.77131167976407\n            ],\n            [\n              -75.41015624999999,\n              42.09822241118974\n            ],\n            [\n              -79.5849609375,\n              42.06560675405716\n            ],\n            [\n              -79.9365234375,\n              42.293564192170095\n            ],\n            [\n              -80.6396484375,\n              41.672911819602085\n            ],\n            [\n              -80.6396484375,\n              40.1452892956766\n            ],\n            [\n              -81.474609375,\n              39.232253141714885\n            ],\n            [\n              -81.8701171875,\n              38.92522904714054\n            ],\n            [\n              -82.5732421875,\n              38.44498466889473\n            ],\n            [\n              -82.2216796875,\n              37.43997405227057\n            ],\n            [\n              -83.5400390625,\n              36.63316209558658\n            ],\n            [\n              -76.2451171875,\n              36.56260003738545\n            ],\n            [\n              -73.47656249999999,\n              34.30714385628804\n            ],\n            [\n              -70.6640625,\n              35.137879119634185\n            ],\n            [\n              -72.333984375,\n              40.212440718286466\n            ],\n            [\n              -73.8720703125,\n              40.48038142908172\n            ],\n            [\n              -74.6630859375,\n              39.027718840211605\n            ],\n            [\n              -75.6298828125,\n              39.470125122358176\n            ],\n            [\n              -75.5859375,\n              39.90973623453719\n            ],\n            [\n              -74.92675781249999,\n              40.1452892956766\n            ],\n            [\n              -75.234375,\n              40.48038142908172\n            ],\n            [\n              -74.70703125,\n              41.44272637767212\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":796080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Jay R.","contributorId":238115,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","middleInitial":"R.","affiliations":[],"preferred":false,"id":796081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beal, Yen-Ju G. 0000-0002-5538-5687 ygbeal@usgs.gov","orcid":"https://orcid.org/0000-0002-5538-5687","contributorId":5328,"corporation":false,"usgs":true,"family":"Beal","given":"Yen-Ju","email":"ygbeal@usgs.gov","middleInitial":"G.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeVries, Ben 0000-0003-2136-3401","orcid":"https://orcid.org/0000-0003-2136-3401","contributorId":198971,"corporation":false,"usgs":false,"family":"DeVries","given":"Ben","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":796083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hwang, Nora","contributorId":238116,"corporation":false,"usgs":false,"family":"Hwang","given":"Nora","email":"","affiliations":[],"preferred":false,"id":796085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mazzarella, Christine","contributorId":169818,"corporation":false,"usgs":false,"family":"Mazzarella","given":"Christine","email":"","affiliations":[],"preferred":false,"id":796086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70209111,"text":"sir20205028 - 2020 - Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17","interactions":[],"lastModifiedDate":"2020-05-06T11:32:05.924687","indexId":"sir20205028","displayToPublicDate":"2020-05-05T14:18:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5028","displayTitle":"Simulation of Discharge, Water-Surface Elevations, and Water Temperatures for the St. Louis River Estuary, Minnesota-Wisconsin, 2016–17","title":"Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17","docAbstract":"<p>The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling.</p><p>The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data.</p><p>The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent<br>agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17.</p><p>Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary.</p><p>From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value.</p><p>The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205028","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Smith, E.A., Kiesling, R.L., and Hayter, E.J., 2020, Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17: U.S. Geological Survey Scientific Investigations Report 2020–5028, 31 p., https://doi.org/10.3133/sir20205028.","productDescription":"Report: viii, 31 p.; Data Release; Dataset","onlineOnly":"Y","ipdsId":"IP-113167","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437002,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U1XXG0","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC model scenarios for velocity profiles around Munger Landing, selected years (2012-2019)"},{"id":374450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5028/coverthb.jpg"},{"id":374451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5028/sir20205028.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5028"},{"id":374452,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990OUS6","text":"USGS data release","linkHelpText":"St. Louis River estuary (Minnesota-Wisconsin) EFDC hydrodynamic model for discharge and temperature simulations: 2016–17"},{"id":374455,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System—","linkHelpText":"USGS Water Data for the Nation"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River estuary","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -92.548828125,\n              46.62869257083747\n            ],\n            [\n              -92.0050048828125,\n              46.62869257083747\n            ],\n            [\n              -92.0050048828125,\n              47.07199249565323\n            ],\n            [\n              -92.548828125,\n              47.07199249565323\n            ],\n            [\n              -92.548828125,\n              46.62869257083747\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/umid-water\" href=\"https://www.usgs.gov/centers/umid-water\">Upper Midwest Water Science Center</a> <br>U.S. Geological Survey <br>2280 Woodale Drive <br>Mounds View, MN 55112</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Model Calibration and Results</li><li>Model Limitations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2020-05-05","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayter, Earl J.","contributorId":223403,"corporation":false,"usgs":false,"family":"Hayter","given":"Earl","email":"","middleInitial":"J.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":784964,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70209891,"text":"ofr20201034 - 2020 - Prioritizing habitats based on abundance and distribution of molting waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska","interactions":[],"lastModifiedDate":"2020-05-06T11:27:56.608874","indexId":"ofr20201034","displayToPublicDate":"2020-05-05T11:48:45","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1034","displayTitle":"Prioritizing Habitats based on Abundance and Distribution of Molting Waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska","title":"Prioritizing habitats based on abundance and distribution of molting waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska","docAbstract":"<p>The National Petroleum Reserve in Alaska (NPR-A) encompasses more than 9.5 million hectares of federally managed land on the Arctic Coastal Plain of northern Alaska, where it supports a diversity of wildlife, including millions of migratory birds. Within the NPR-A, Teshekpuk Lake and the surrounding area provide important habitat for migratory birds, including large numbers of waterfowl and shorebirds that use the area for breeding and molting. This area has been designated by the Bureau of Land Management as the Teshekpuk Lake Special Area (TLSA) and is estimated to host 22 percent of the entire Pacific black brant (<i>Branta bernicla nigricans</i>) population as it undergoes flightless wing molt. Additionally, numerous other waterfowl species use the area for breeding and molting, including greater white-fronted geese (<i>Anser albifrons</i>), snow geese (<i>Chen caerulescens</i>), Canada geese (<i>Branta hutchinsii</i>), and tundra swans (<i>Cygnus columbianus</i>). A data-derived procedure was developed to define important habitats based on recent distributions of molting birds. That procedure was used to identify areas that could be prioritized for exclusion from oil and gas development within a pre-defined “Goose Molting Area” in the TLSA. This analysis was requested by the Bureau of Land Management to provide information for the development of alternative scenarios for an updated NPR-A, Integrated Activity Plan/Environmental Impact Statement. Habitat selections were based on the population densities of Pacific black brant and Canada geese and pre-defined thresholds for the minimum fraction of the population contained within selected areas. Selections were based on long-term records of population density combined with global-positioning system data to reveal small-scale patterns of habitat use. The highest population density of the Pacific black brant was found along the Beaufort Sea coast on the eastern edge of the study area, whereas Canada geese were somewhat more widely distributed. Depending on the selection criteria and width of protective buffers placed around selected habitat units, 52–85 percent of the Goose Molting Area was identified as high-priority habitat. The effectiveness of this approach to habitat protection assumes that buffers around selected habitat units are wide enough to provide adequate protection from disturbance related to oil and gas development. This assumption remained a key source of uncertainty that could be addressed through additional study of disturbance effects on molting waterfowl.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201034","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Flint, P.L., Patil, V., Shults, B., and Thompson, S.J., 2020, Prioritizing habitats based on abundance and distribution of molting waterfowl in the Teshekpuk Lake Special Area of the National Petroleum Reserve, Alaska: U.S. Geological Survey Open-File Report 2020-1034, 16 p., https://doi.org/10.3133/ofr20201034.","productDescription":"Report: iv, 16 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-115467","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":374468,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZGNRTB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Habitat selection scenarios for molting waterfowl in the Goose Molting Area of the Teshekpuk Lake Special Area, for NPR-A Integrated Activity Plan/Environmental Impact Statement (2020)"},{"id":374466,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1034/coverthb.jpg"},{"id":374467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1034/ofr20201034.pdf","text":"Report","size":"3.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1034"}],"country":"United States","state":"Alaska","otherGeospatial":"National Petroleum Reserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -154.59411621093747,\n              70.23346027955571\n            ],\n            [\n              -151.7816162109375,\n              70.23346027955571\n            ],\n            [\n              -151.6717529296875,\n              70.52306573985297\n            ],\n            [\n              -152.0892333984375,\n              70.8248355501024\n            ],\n            [\n              -153.0670166015625,\n              70.95790503334285\n            ],\n            [\n              -154.59411621093747,\n              70.86448996613296\n            ],\n            [\n              -154.59411621093747,\n              70.23346027955571\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ak@usgs.gov\" data-mce-href=\"mailto:dc_ak@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/asc/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/asc/\">Alaska Science Center</a><br>U.S. Geological Survey<br>4210 University Drive<br>Anchorage, Alaska 99508</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Objectives</li><li>Description of Available Data</li><li>Methods</li><li>Molt-Unit Buffers</li><li>Results</li><li>Discussion</li><li>Conclusions</li><li>References Cited</li></ul>","publishedDate":"2020-05-05","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":788496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patil, Vijay 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":224481,"corporation":false,"usgs":false,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":788497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shults, Bradley","contributorId":224468,"corporation":false,"usgs":false,"family":"Shults","given":"Bradley","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":788498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Sarah J. 0000-0002-5733-8198 sjthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-5733-8198","contributorId":5434,"corporation":false,"usgs":true,"family":"Thompson","given":"Sarah","email":"sjthompson@usgs.gov","middleInitial":"J.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":788499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70220547,"text":"70220547 - 2020 - Combining genetic and demographic monitoring better informs conservation of an endangered urban snake","interactions":[],"lastModifiedDate":"2025-04-16T13:18:39.30065","indexId":"70220547","displayToPublicDate":"2020-05-05T08:10:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Combining genetic and demographic monitoring better informs conservation of an endangered urban snake","docAbstract":"<div class=\"abstract toc-section abstract-type-\"><div class=\"abstract-content\"><p>Conversion and fragmentation of wildlife habitat often leads to smaller and isolated populations and can reduce a species’ ability to disperse across the landscape. As a consequence, genetic drift can quickly lower genetic variation and increase vulnerability to extirpation. For species of conservation concern, quantification of population size and connectivity can clarify the influence of genetic drift in local populations and provides important information for conservation management and recovery strategies. Here, we used genome-wide single nucleotide polymorphism (SNP) data and capture-mark-recapture methods to evaluate the genetic diversity and demography within seven focal sites of the endangered San Francisco gartersnake (<i>Thamnophis sirtalis tetrataenia</i>), a species affected by alteration and isolation of wetland habitats throughout its distribution. The primary goals were to determine the population structure and degree of genetic isolation among<span>&nbsp;</span><i>T</i>.<span>&nbsp;</span><i>s</i>.<span>&nbsp;</span><i>tetrataenia</i><span>&nbsp;</span>populations and estimate effective size and population abundance within sites to better understand the present and future importance of genetic drift. We also used temporally sampled datasets to examine the magnitude of genetic change over time. We found moderate population genetic structure throughout the San Francisco Peninsula that partitions sites into northern and southern regional clusters. Point estimates of both effective size and population abundance were generally small (≤ 100) for a majority of the sites, and estimates were particularly low in the northern populations. Genetic analyses of temporal datasets indicated an increase in genetic differentiation, especially for the most geographically isolated sites, and decreased genetic diversity over time in at least one site (Pacifica). Our results suggest that drift-mediated processes as a function of small population size and reduced connectivity from neighboring populations may decrease diversity and increase differentiation. Improving genetic diversity and connectivity among<span>&nbsp;</span><i>T</i>.<span>&nbsp;</span><i>s</i>.<span>&nbsp;</span><i>tetrataenia</i><span>&nbsp;</span>populations could promote persistence of this endangered snake.</p></div></div><div id=\"figure-carousel-section\"><br></div>","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0231744","usgsCitation":"Wood, D.A., Rose, J.P., Halstead, B., Stoelting, R.E., Swaim, K.E., and Vandergast, A.G., 2020, Combining genetic and demographic monitoring better informs conservation of an endangered urban snake: PLoS ONE, v. 15, no. 5, e0231744, 27 p.; Data Release, https://doi.org/10.1371/journal.pone.0231744.","productDescription":"e0231744, 27 p.; Data Release","ipdsId":"IP-114654","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458808,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0231744","text":"Publisher Index Page"},{"id":437231,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YKLBB5","text":"USGS data release","linkHelpText":"San Francisco Gartersnake (Thamnophis sirtalis tetrataenia) Genomic and Demographic Data from San Mateo County and Northeastern Santa Cruz County Collected Between 2016 - 2018"},{"id":385763,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.62939453125001,\n              37.243448378654115\n            ],\n            [\n              -122.05261230468751,\n              37.243448378654115\n            ],\n            [\n              -122.05261230468751,\n              37.81846319511331\n            ],\n            [\n              -122.62939453125001,\n              37.81846319511331\n            ],\n            [\n              -122.62939453125001,\n              37.243448378654115\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":815969,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stoelting, Ricka E.","contributorId":171533,"corporation":false,"usgs":false,"family":"Stoelting","given":"Ricka","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":815970,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swaim, Karen E","contributorId":258210,"corporation":false,"usgs":false,"family":"Swaim","given":"Karen","email":"","middleInitial":"E","affiliations":[{"id":52239,"text":"Swaim Biological Incorporated","active":true,"usgs":false}],"preferred":false,"id":815971,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815972,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70209896,"text":"70209896 - 2020 - Ringed seal (Pusa hispida) seasonal movements, diving, and haul-out behavior in the Beaufort, Chukchi, and Bering Seas (2011–2017)","interactions":[],"lastModifiedDate":"2020-07-09T14:55:32.503474","indexId":"70209896","displayToPublicDate":"2020-05-05T07:05:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Ringed seal (Pusa hispida) seasonal movements, diving, and haul-out behavior in the Beaufort, Chukchi, and Bering Seas (2011–2017)","docAbstract":"Continued Arctic warming and sea-ice loss will have important implications for the conservation of ringed seals, a highly ice-dependent species. A better understanding of their spatial ecology will help characterize emerging ecological trends and inform management decisions. We deployed satellite transmitters on ringed seals in the summers of 2011, 2014, and 2016 near Utqiaġvik (formerly Barrow), Alaska to monitor their movements, diving, and haul-out behavior. We present analyses of tracking and dive data provided by 17 seals that were tracked until at least January of the following year. Seals mostly ranged north of Utqiaġvik in the Beaufort and Chukchi Seas during summer before moving into the southern Chukchi and Bering Seas during winter. In all seasons, ringed seals occupied a diversity of habitats and spatial distributions; from near shore and localized, to far offshore and wide-ranging in drifting sea-ice. Continental shelf waters were occupied for >96% of tracking-days, during which repetitive-diving (suggestive of foraging) primarily to the seafloor was the most frequent activity. From mid-summer to early-fall, 12 seals made ~ one-week forays off-shelf to the deep Arctic Basin, most reaching the retreating pack-ice, where they spent most of their time hauled out. Diel activity patterns suggested greater allocation of foraging efforts to midday hours. Haul-out patterns were complementary, occurring mostly at night until April-May when midday hours were preferred. Ringed seals captured in 2011—concurrent with an unusual mortality event (UME) that affected all ice seal species—differed morphologically and behaviorally from seals captured in other years. Speculations about the physiology of molting and its role in energetics, habitat use, and behavior are discussed; along with possible evidence of purported ringed seal ecotypes.","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6302","usgsCitation":"Von Duyke, A.L., Douglas, D.C., Herreman, J.K., and Crawford, J.A., 2020, Ringed seal (Pusa hispida) seasonal movements, diving, and haul-out behavior in the Beaufort, Chukchi, and Bering Seas (2011–2017): Ecology and Evolution, v. 10, no. 12, p. 5595-5616, https://doi.org/10.1002/ece3.6302.","productDescription":"21 p.","startPage":"5595","endPage":"5616","ipdsId":"IP-102815","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":456850,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.6302","text":"Publisher Index Page"},{"id":374482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea, Chukchi Sea, Bering Sea","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -141.50390625,\n              69.59589006237648\n            ],\n            [\n              -141.6796875,\n              70.67088107015755\n            ],\n            [\n              -149.94140625,\n              73.32785809840696\n            ],\n            [\n              -161.19140625,\n              73.02259157147301\n            ],\n            [\n              -168.57421875,\n              71.24435551310674\n            ],\n            [\n              -168.75,\n              64.92354174306496\n            ],\n            [\n              -170.68359375,\n              53.330872983017066\n            ],\n            [\n              -166.9921875,\n              51.6180165487737\n            ],\n            [\n              -158.73046875,\n              55.07836723201515\n            ],\n            [\n              -156.796875,\n              58.81374171570782\n            ],\n            [\n              -158.55468749999997,\n              63.31268278043484\n            ],\n            [\n              -161.89453125,\n              69.16255790810501\n            ],\n            [\n              -157.32421875,\n              71.13098770917023\n            ],\n            [\n              -141.50390625,\n              69.59589006237648\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Von Duyke, Andrew L.","contributorId":214208,"corporation":false,"usgs":false,"family":"Von Duyke","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":38995,"text":"North Slope Borough Department of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":788535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":788536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herreman, Jason K","contributorId":224482,"corporation":false,"usgs":false,"family":"Herreman","given":"Jason","email":"","middleInitial":"K","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":788537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crawford, Justin A.","contributorId":214225,"corporation":false,"usgs":false,"family":"Crawford","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":788538,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228157,"text":"70228157 - 2020 - Understanding nekton use of estuarine habitats in the northern Gulf of Mexico: Guidebook for natural resource managers and restoration practitioners","interactions":[],"lastModifiedDate":"2022-02-07T17:24:48.015074","indexId":"70228157","displayToPublicDate":"2020-05-04T11:19:47","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10087,"text":"Guidebook","active":true,"publicationSubtype":{"id":3}},"title":"Understanding nekton use of estuarine habitats in the northern Gulf of Mexico: Guidebook for natural resource managers and restoration practitioners","docAbstract":"<p><span>Without a comprehensive understanding of nekton use of key habitats across locations, natural resource managers and restoration practitioners in the northern Gulf of Mexico region lack a key tool to assist in their efforts to design, implement, and monitor effective coastal restoration and protection efforts in the decades to come. To address this need, Abt helped conduct a systematic literature review, data compilation, and meta-analysis to evaluate nekton use of estuarine habitats in the northern Gulf of Mexico.</span></p>","language":"English","publisher":"Abt Associates","usgsCitation":"Hollweg, T.A., Christman, M., Sauby, K., Cebrian, J., and La Peyre, M., 2020, Understanding nekton use of estuarine habitats in the northern Gulf of Mexico: Guidebook for natural resource managers and restoration practitioners: Guidebook, 154 p.","productDescription":"154 p.","ipdsId":"IP-108690","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395512,"type":{"id":15,"text":"Index Page"},"url":"https://www.abtassociates.com/insights/publications/report/understanding-nekton-use-of-estuarine-habitats-in-the-northern-gulf-of"}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Gulf of Mexico coast","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -91.99951171875,\n              29.48742484748479\n            ],\n            [\n              -92.64770507812499,\n              29.897805610155874\n            ],\n            [\n              -93.812255859375,\n              29.973970240516614\n            ],\n            [\n              -94.7900390625,\n              29.897805610155874\n            ],\n            [\n              -95.460205078125,\n              29.34387539941801\n            ],\n            [\n              -95.80078125,\n              29.017748018496047\n            ],\n            [\n              -96.8994140625,\n              28.738763971370293\n            ],\n            [\n              -97.547607421875,\n              27.9361805667694\n            ],\n            [\n              -97.679443359375,\n              27.088473156555896\n            ],\n            [\n              -97.23999023437499,\n              26.96124577052697\n            ],\n            [\n              -95.80078125,\n              28.51696944040106\n            ],\n            [\n              -94.405517578125,\n              29.35345166863502\n            ],\n            [\n              -93.021240234375,\n              29.544787796199465\n            ],\n            [\n              -91.99951171875,\n              29.48742484748479\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hollweg, T. A.","contributorId":274733,"corporation":false,"usgs":false,"family":"Hollweg","given":"T.","email":"","middleInitial":"A.","affiliations":[{"id":56644,"text":"Abt Associates","active":true,"usgs":false}],"preferred":false,"id":833264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christman, M. C.","contributorId":274734,"corporation":false,"usgs":false,"family":"Christman","given":"M. C.","affiliations":[{"id":56647,"text":"MCC Statistical Consulting","active":true,"usgs":false}],"preferred":false,"id":833265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sauby, K.","contributorId":274735,"corporation":false,"usgs":false,"family":"Sauby","given":"K.","email":"","affiliations":[{"id":56647,"text":"MCC Statistical Consulting","active":true,"usgs":false}],"preferred":false,"id":833266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cebrian, J.","contributorId":243394,"corporation":false,"usgs":false,"family":"Cebrian","given":"J.","affiliations":[{"id":48710,"text":"University of South Alabama","active":true,"usgs":false}],"preferred":false,"id":833267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833268,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70213227,"text":"70213227 - 2020 - Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces","interactions":[],"lastModifiedDate":"2020-09-15T12:56:38.466452","indexId":"70213227","displayToPublicDate":"2020-05-02T07:41:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces","docAbstract":"Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high‐, medium‐ and coarse‐spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI‐based ET, over a 780‐ha urban park in Adelaide, Australia. We validated ET with the ground‐based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long‐term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET‐MODIS and ET‐Landsat (R2 > 0.99 for all VIs). Landsat‐VIs captured the seasonal changes of greenness better than MODIS‐VIs. We used artificial neural network (ANN) to relate the RS‐ET and ground data, and ET‐MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS‐ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water‐limited cities.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13790","usgsCitation":"Nouri, H., Nagler, P.L., Borujeni, S.C., Munez, A.B., Alaghmand, S., Noori, B., Galindo, A., and Didan, K., 2020, Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces: Hydrological Processes, v. 34, no. 15, p. 3183-3199, https://doi.org/10.1002/hyp.13790.","productDescription":"17 p.","startPage":"3183","endPage":"3199","ipdsId":"IP-110995","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":456880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13790","text":"Publisher Index Page"},{"id":378390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","city":"Adelaide","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              138.4716796875,\n              -35.06597313798418\n            ],\n            [\n              138.955078125,\n              -35.06597313798418\n            ],\n            [\n              138.955078125,\n              -34.70549341022545\n            ],\n            [\n              138.4716796875,\n              -34.70549341022545\n            ],\n            [\n              138.4716796875,\n              -35.06597313798418\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"34","issue":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nouri, Hamideh 0000-0002-7424-5030","orcid":"https://orcid.org/0000-0002-7424-5030","contributorId":16327,"corporation":false,"usgs":true,"family":"Nouri","given":"Hamideh","email":"","affiliations":[],"preferred":false,"id":798683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borujeni, Sattar Chavoshi","contributorId":240671,"corporation":false,"usgs":false,"family":"Borujeni","given":"Sattar","email":"","middleInitial":"Chavoshi","affiliations":[],"preferred":false,"id":798684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munez, Armando Barreto","contributorId":240672,"corporation":false,"usgs":false,"family":"Munez","given":"Armando","email":"","middleInitial":"Barreto","affiliations":[],"preferred":false,"id":798685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alaghmand, Sina","contributorId":172388,"corporation":false,"usgs":false,"family":"Alaghmand","given":"Sina","email":"","affiliations":[{"id":27031,"text":"School of Natural and Built Environments, U. So. Aus and Discipline of Civil Engineering, School Of Engineering, Monash University Malaysia","active":true,"usgs":false}],"preferred":false,"id":798686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Noori, Behnaz","contributorId":172392,"corporation":false,"usgs":false,"family":"Noori","given":"Behnaz","email":"","affiliations":[],"preferred":false,"id":798687,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galindo, Alejandro","contributorId":240673,"corporation":false,"usgs":false,"family":"Galindo","given":"Alejandro","email":"","affiliations":[],"preferred":false,"id":798688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798689,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70209775,"text":"sim3456 - 2020 - Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–19","interactions":[],"lastModifiedDate":"2021-10-29T18:54:38.458013","indexId":"sim3456","displayToPublicDate":"2020-05-01T13:45:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3456","displayTitle":"Elevation and Elevation-Change Maps of Fountain Creek, Southeastern Colorado, 2015–19","title":"Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–19","docAbstract":"<p>The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, has been collecting topographic data at 10 study areas along Fountain Creek, Colorado, annually since 2012. The 10 study areas are located between Colorado Springs and the terminus of Fountain Creek at the Arkansas River in Pueblo. The purpose of this report is to present elevation maps based on topographic surveys collected in 2015 and 2019 and to present maps of elevation change that occurred between 2015 and 2019 at all 10 study areas. Elevation and elevation-change maps were developed in ArcGIS from topographic surveys collected at each study area using real-time kinematic Global Navigation Satellite Systems during the winter months (January through April) of 2015 and 2019. Elevation-change maps were created using statistically defined minimum levels of change detection asso-ciated with the 68-percent confidence limit and the 95-percent confidence limit. Study areas along Fountain Creek underwent a range of geomorphic responses between 2015 and 2019 that often depended on the dominant channel pattern of the study area. The results of this ongoing monitoring effort can be used to assess long-term changes in land-surface elevation and to advance understanding of the geomorphic response to possible alterations in flow conditions on Fountain Creek. </p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3456","collaboration":"Prepared in cooperation with Colorado Springs Utilities","usgsCitation":"Hempel, L., 2020, Elevation and elevation-change maps of Fountain Creek, southeastern Colorado, 2015–19:  \nU.S. Geological Survey Scientific Investigations Map 3456, 10 sheets, 9 p., https://doi.org/10.3133/sim3456.","productDescription":"Report: vi, 9 p.; 11 Sheets: 12.20 x 13.45 inches or smaller; Read Me; Data Release","onlineOnly":"Y","ipdsId":"IP-112462","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":391173,"rank":16,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3481","text":"Elevation and Elevation-Change Maps of Fountain Creek, Southeastern Colorado, 2015–20"},{"id":374401,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R00MWF","text":"USGS data release","linkHelpText":"Topographic and Sediment Size Data from Fountain Creek between Colorado Springs and the Confluence with the Arkansas River, Colorado, 2019"},{"id":374374,"rank":14,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_ReadMe.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3456 Read Me"},{"id":374298,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet9.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 09","size":"39.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 09","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374296,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet7.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 07","size":"32.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 07","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374295,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet6.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 06","size":"33.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 06","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374297,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet8.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 08","size":"41.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 08","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374291,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet2.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 02","size":"30.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 02","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374288,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3456/coverthb2.jpg"},{"id":374289,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_pamphlet.pdf","text":"Report","size":"3.58 M","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3456 pamphlet"},{"id":374292,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet3.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 03","size":"30.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 03","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374290,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet1.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 01","size":"34.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 01","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374293,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet4.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 04","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 04","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374294,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet5.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 05","size":"33.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Eevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 05","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374299,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheet10.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 10","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Area 10","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."},{"id":374300,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3456/sim3456_sheets1to10.pdf","text":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Areas 01-10","size":"345 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Elevation (2015, 2019) and Elevation-Change (2015−19) Maps—Study Areas 01-10","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC  to access interactive layers."}],"country":"United States","state":"Colorado","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -105.00457763671874,\n              38.35027253825765\n            ],\n            [\n              -104.5404052734375,\n              38.35027253825765\n            ],\n            [\n              -104.5404052734375,\n              39.15988184949157\n            ],\n            [\n              -105.00457763671874,\n              39.15988184949157\n            ],\n            [\n              -105.00457763671874,\n              38.35027253825765\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"http://www.usgs.gov/centers/co-water/\" data-mce-href=\"http://www.usgs.gov/centers/co-water/\">Colorado Water Science Center</a><br>U.S. Geological Survey<br>Box 25046, MS-415<br>Denver, CO 80225-0046</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Study Area</li><li>Methods</li><li>Elevation-Change Maps and Geomorphic Changes</li><li>Summary</li><li>References Cited</li></ul>","publishedDate":"2020-05-01","noUsgsAuthors":false,"publicationDate":"2020-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":787958,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70228790,"text":"70228790 - 2020 - The relationship between biodiversity and wetland cover varies across regions of the conterminous United States","interactions":[],"lastModifiedDate":"2022-02-21T15:35:17.141252","indexId":"70228790","displayToPublicDate":"2020-05-01T09:21:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The relationship between biodiversity and wetland cover varies across regions of the conterminous United States","docAbstract":"Identifying the factors that determine the spatial distribution of biodiversity is a major focus of ecological research. These factors vary with scale from interspecific interactions to global climatic cycles. Wetlands are important biodiversity hotspots and contributors of ecosystem services, but the association between proportional wetland cover and species richness has shown mixed results. It is not well known as to what extent there is a relationship between proportional wetland cover and species richness, especially at the sub-continental scale. We used the National Wetlands Inventory to model wetland cover for the conterminous United States and the National Land Cover Database to estimate wetland change between 2001 and 2011. We used a Bayesian spatial Poisson model to estimate a spatially varying coefficient surface describing the effect of proportional wetland cover on the distribution of amphibians, birds, mammals, and reptiles and the cumulative distribution of terrestrial endemic species. Species richness and wetland cover were significantly correlated, and this relationship varied both spatially and by taxonomic group. Rather than a continental-scale association, however, we found that this relationship changed more closely among ecoregions. The species richness of each of the five groups was positively associated with wetland cover in some or all of the Great Plains; additionally, a positive association was found for mammals in the Southeastern Plains and Piedmont of the eastern U.S. Model results indicated negative association especially in the Cold Deserts and Northern Lakes & Forests of Minnesota and Wisconsin, though these varied greatly between groups. Our results highlight the need for wetland conservation initiatives that focus efforts at the level II and III ecoregional scale rather than along political boundaries. ","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0232052","usgsCitation":"Dertien, J.S., Self, S., Ross, B., Barrett, K., and Baldwin, R.F., 2020, The relationship between biodiversity and wetland cover varies across regions of the conterminous United States: PLoS ONE, v. 15, no. 5, p. 1-18, https://doi.org/10.1371/journal.pone.0232052.","productDescription":"e0232052, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-114472","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456890,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0232052","text":"Publisher Index 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University","active":true,"usgs":false}],"preferred":false,"id":835518,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Dertien, Jeremy S.","contributorId":279799,"corporation":false,"usgs":false,"family":"Dertien","given":"Jeremy","email":"","middleInitial":"S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":835487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Self, Stella","contributorId":279800,"corporation":false,"usgs":false,"family":"Self","given":"Stella","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":835488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrett, Kyle","contributorId":149401,"corporation":false,"usgs":false,"family":"Barrett","given":"Kyle","email":"","affiliations":[],"preferred":false,"id":835491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baldwin, Robert F.","contributorId":96415,"corporation":false,"usgs":true,"family":"Baldwin","given":"Robert","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":835489,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70228397,"text":"70228397 - 2020 - Using the Delphi process to gather information from a Bald Eagle expert panel","interactions":[],"lastModifiedDate":"2022-02-10T14:49:41.948517","indexId":"70228397","displayToPublicDate":"2020-05-01T08:44:45","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SWAN.NRR-2020/2128","title":"Using the Delphi process to gather information from a Bald Eagle expert panel","docAbstract":"<p>Bald eagle (<i>Haliaeetus leucocephalus</i>) populations are classified by the Southwest Alaska Network (SWAN) of the National Park Service as a vital sign of biological integrity, largely because of their importance as an indicator species for environmental contaminants and human disturbance. Though Bald Eagles are plentiful in Alaska, it is still imperative to have a monitoring plan that allows for the estimation of population sizes and detection of significant changes in populations. Currently, Bald Eagles are monitored in Kenai Fjords National Park, Katmai National Park and Preserve, Lake Clark National Park and Preserve, and Wrangell – St. Elias National Park, but each park uses different monitoring procedures and evaluation criteria. This makes it difficult for scientists and managers to compare data, detect changes in overall populations, and make effective management decisions. Our research is using a formal structured decision-making process to ensure that the Bald Eagle monitoring conducted by the parks is standardized and meets programmatic goals and objectives. We implemented a Delphi process, which is an iterative survey technique that is used to gather expert opinion. We used online questionnaires to gather information and opinions from National Park Service scientists and managers, eagle experts, and other interested parties. We identified important stressors and feasible monitoring metrics, which were tied to the means objectives for the Bald Eagle monitoring program: minimize cost, minimize effort, maximize ability to detect change in populations, and maximize accurate information about Bald Eagles. We will also analyze monitoring metrics using a consequence table, which determines the performance of each objective in terms of the means objectives chosen by expert panelists. This information will help to create a more accurate conceptual model of the system to guide development of a Bald Eagle monitoring program that can be standardized among Southwest Alaska National Parks.</p>","language":"English","publisher":"National Park Service","usgsCitation":"Kolstrom, R., Wilson, T., and Gigliotti, L.M., 2020, Using the Delphi process to gather information from a Bald Eagle expert panel: Natural Resource Report NPS/SWAN.NRR-2020/2128, vii, 57 p.","productDescription":"vii, 57 p.","ipdsId":"IP-111246","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395766,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/640030"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.65429687499997,\n              54.54657953840501\n            ],\n            [\n              -134.736328125,\n              54.54657953840501\n            ],\n            [\n              -134.736328125,\n              61.501734289732326\n            ],\n            [\n              -155.65429687499997,\n              61.501734289732326\n            ],\n            [\n              -155.65429687499997,\n              54.54657953840501\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kolstrom, Rebecca","contributorId":275658,"corporation":false,"usgs":false,"family":"Kolstrom","given":"Rebecca","email":"","affiliations":[],"preferred":false,"id":834198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L.","contributorId":275659,"corporation":false,"usgs":false,"family":"Wilson","given":"Tammy L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":834199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834200,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70209903,"text":"70209903 - 2020 - Using small unmanned aircraft systems for measuring post-flood high-water marks and streambed elevations","interactions":[],"lastModifiedDate":"2020-05-06T12:20:26.195737","indexId":"70209903","displayToPublicDate":"2020-05-01T07:17:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Using small unmanned aircraft systems for measuring post-flood high-water marks and streambed elevations","docAbstract":"Floods affected approximately two billion people around the world from 1998–2017, causing over 142,000 fatalities and over 656 billion U.S. dollars in economic losses. Flood data, such as the extent of inundation and peak flood stage, are needed to define the environmental, economic, and social impacts of significant flood events. Ground-based global positioning system (GPS) surveys of post-flood high-water marks (HWMs) and topography are commonly used to define flood inundation and stage, but can be time consuming, difficult, and expensive to conduct. Here, we demonstrate and test the use of small unmanned aircraft systems (sUAS) and close-range remote sensing techniques to collect high-accuracy flood data to define peak flood stage elevations and river cross sections. We evaluate the elevation accuracy of the HWMs from sUAS surveys by comparison with traditional GPS surveys, which have acceptable accuracy for many post-flood assessments, at two flood sites on two small streams in the United States. Mean elevation errors for the sUAS surveys were 0.07 m and 0.14 m for the semiarid and temperate sites respectively, and those values are similar to typical errors when measuring HWM elevations with GPS surveys. Results demonstrate that sUAS surveys of HWMs and cross sections can be an inexpensive and efficient alternative to GPS surveys, and we provide insights that can be used to decide whether sUAS or GPS techniques will be most efficient for post-flood surveying.","language":"English","publisher":"MDPI","doi":"10.3390/rs12091437","collaboration":"","usgsCitation":"Forbes, B.T., DeBenedetto, G., Dickinson, J.E., Bunch, C., and Fitzpatrick, F., 2020, Using small unmanned aircraft systems for measuring post-flood high-water marks and streambed elevations: Remote Sensing, v. 12, no. 9, 1437, 22 p., https://doi.org/10.3390/rs12091437.","productDescription":"1437, 22 p.","ipdsId":"IP-115597","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":456891,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091437","text":"Publisher Index Page"},{"id":437008,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NFR2TQ","text":"USGS data release","linkHelpText":"04087088 - Underwood Creek at Wauwatosa, WI - 2019/07/17 GPS Survey"},{"id":437007,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZNN0Z5","text":"USGS data release","linkHelpText":"04087088 - Underwood Creek at Wauwatosa, WI - 2018/09/14 GPS Survey"},{"id":437006,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TRESCN","text":"USGS data release","linkHelpText":"09487000 - Brawley Wash near Three Points, AZ - 2018/09/19 GPS Survey"},{"id":374485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Forbes, Brandon T. 0000-0003-4051-0593 bforbes@usgs.gov","orcid":"https://orcid.org/0000-0003-4051-0593","contributorId":213549,"corporation":false,"usgs":true,"family":"Forbes","given":"Brandon","email":"bforbes@usgs.gov","middleInitial":"T.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeBenedetto, Geoffrey 0000-0003-0696-4567 gdebened@usgs.gov","orcid":"https://orcid.org/0000-0003-0696-4567","contributorId":220988,"corporation":false,"usgs":true,"family":"DeBenedetto","given":"Geoffrey","email":"gdebened@usgs.gov","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunch, Claire 0000-0002-1360-8598","orcid":"https://orcid.org/0000-0002-1360-8598","contributorId":220987,"corporation":false,"usgs":true,"family":"Bunch","given":"Claire","email":"","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":788560,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":173463,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","email":"fafitzpa@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":false,"id":788561,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70210149,"text":"70210149 - 2020 - Individual and population fitness consequences associated with large carnivore use of residential development","interactions":[],"lastModifiedDate":"2020-05-18T12:15:44.247337","indexId":"70210149","displayToPublicDate":"2020-05-01T07:11:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Individual and population fitness consequences associated with large carnivore use of residential development","docAbstract":"Large carnivores are negotiating increasingly developed landscapes, but little is known about how such behavioral plasticity influences their demographic rates and population trends. Some investigators have suggested that the ability of carnivores to behaviorally adapt to human development will enable their persistence, and yet, others have suggested that such landscapes are likely to serve as population sinks or ecological traps. To understand how plasticity in black bear (Ursus americanus) use of residential development influences their population dynamics, we conducted a 6 year study near Durango, Colorado, USA. Using space-use data on individual bears, we examined the influence of use of residential development on annual measures of bear body fat, cub productivity, cub survival and adult female survival, after accounting for variation in natural food availability and individual attributes (e.g., age). We then used our field-based vital rate estimates to parameterize a matrix model that simulated asymptotic population growth for bears using residential development to different degrees. We found that bear use of residential development was highly variable within and across years, with bears increasing their foraging within development when natural foods were scarce. Increased bear use of development was associated with increased body fat and cub productivity, but reduced cub and adult survival. When these effects were simultaneously incorporated into a matrix model we found that the population was projected to decline as bear use of development increased, given that the costs of reduced survival outweighed the benefits of enhanced productivity. Our results provide a mechanistic understanding of how black bear use of residential development exerts opposing effects on different bear fitness traits and a negative effect on population growth, with the magnitude of those effects mediated by variation in environmental conditions. They also highlight the importance of monitoring bear population dynamics, particularly as shifts in bear behavior are likely to drive increases in human-bear conflicts and the perception of growing bear populations. Finally, our work emphasizes the need to consider the demographic viability of large carnivore populations when promoting the coexistence of people and carnivores on shared landscapes.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3098","collaboration":"","usgsCitation":"Johnson, H.E., Lewis, D., and Breck, S., 2020, Individual and population fitness consequences associated with large carnivore use of residential development: Ecosphere, v. 11, no. 5, e03098, 23 p., https://doi.org/10.1002/ecs2.3098.","productDescription":"e03098, 23 p.","ipdsId":"IP-111185","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":456893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3098","text":"Publisher Index Page"},{"id":374882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Durango","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -108.11370849609375,\n              37.1165261849112\n            ],\n            [\n              -107.66876220703125,\n              37.1165261849112\n            ],\n            [\n              -107.66876220703125,\n              37.398528132728615\n            ],\n            [\n              -108.11370849609375,\n              37.398528132728615\n            ],\n            [\n              -108.11370849609375,\n              37.1165261849112\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":789314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewis, David Bruce","contributorId":156433,"corporation":false,"usgs":false,"family":"Lewis","given":"David Bruce","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":789315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breck, Stewart","contributorId":199403,"corporation":false,"usgs":false,"family":"Breck","given":"Stewart","affiliations":[],"preferred":false,"id":789316,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70219116,"text":"70219116 - 2020 - Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses","interactions":[],"lastModifiedDate":"2021-04-15T16:28:18.487489","indexId":"70219116","displayToPublicDate":"2020-04-30T11:25:18","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses","docAbstract":"<p>Metamorphic rock fabrics such as foliations and lineations provide a rock record of numerous deformational characteristics in the Earth’s crust. When spatial information is combined with fabric data collected at points on geologic maps, the nature and consistency of metamorphic fabrics can be explored through structural domain analysis. This is particularly useful in large regions where there is not well-established stratigraphy and where bedrock exposures are limited. Domains that contain distinctive orientations and patterns of fabrics can be constructed on the basis of several different parameters, but in folded, polydeformational regions cylindricity can be particularly useful. Distinct domains of cylindrical folding can sometimes be determined where poles to foliations show characteristic patterns on equal-area projections and lie perpendicular to a single axis in space. Additionally, the patterns of elements such as fold axes and mineral lineations can be used in conjunction with foliation data to refine domains and confirm parameters such as coaxiality.&nbsp;</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2020 Cordilleran tectonics workshop program and abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2020 Cordilleran Tectonics Workshop","conferenceDate":"Feb 21-23, 2020","conferenceLocation":"Anchorage, AK","language":"English","publisher":"Cordilleran Tectonics Workshop","usgsCitation":"Caine, J., and Jones, J.V., 2020, Exploring regional scale metamorphic fabrics in the Yukon Tanana terrane and environs using quantitative domain analyses, <i>in</i> 2020 Cordilleran tectonics workshop program and abstracts, Anchorage, AK, Feb 21-23, 2020, p. 11-13.","productDescription":"3 p.","startPage":"11","endPage":"13","ipdsId":"IP-116658","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":385130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384616,"type":{"id":15,"text":"Index Page"},"url":"https://cordillerantectonics.com/program-and-abstracts/"}],"country":"Canada, United States","state":"Alaska, Yukon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -156.59912109375,\n              61.41775026352097\n            ],\n            [\n              -134.14306640625,\n              61.41775026352097\n            ],\n            [\n              -134.14306640625,\n              67.44122869796351\n            ],\n            [\n              -156.59912109375,\n              67.44122869796351\n            ],\n            [\n              -156.59912109375,\n              61.41775026352097\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":812833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, James V. III 0000-0002-6602-5935 jvjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6602-5935","contributorId":201245,"corporation":false,"usgs":true,"family":"Jones","given":"James","suffix":"III","email":"jvjones@usgs.gov","middleInitial":"V.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":812834,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70211321,"text":"70211321 - 2020 - 2019 National park visitor spending effects: Economic contributions to local communities, states, and the nation","interactions":[],"lastModifiedDate":"2020-07-27T15:10:54.41551","indexId":"70211321","displayToPublicDate":"2020-04-30T10:06:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/EQD/NRR—2020/2110","title":"2019 National park visitor spending effects: Economic contributions to local communities, states, and the nation","docAbstract":"<p>The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports economic activity within park gateway communities. This report summarizes the annual economic contribution analysis that measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income. In 2019, the National Park System received over 327.5 million recreation visits. Visitors to national parks spent an estimated \\$21 billion in local gateway regions. The contribution of this spending to the national economy was 340,500 jobs, \\$14.1 billion in labor income, \\$24.3 billion in value added, and \\$41.7 billion in economic output. The lodging sector saw the highest direct effects, with \\$7.1 billion in economic output directly contributed to this sector nationally. The restaurants sector saw the next greatest effects, with $4.2 billion in economic output directly contributed to this sector nationally. Results from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. 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,{"id":70223287,"text":"70223287 - 2020 - Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast","interactions":[],"lastModifiedDate":"2021-08-20T14:19:55.645255","indexId":"70223287","displayToPublicDate":"2020-04-30T09:13:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast","docAbstract":"<p><span>For migratory species such as Black Scoters (</span><i>Melanitta americana</i><span>) whose range encompasses a variety of habitats, it is especially important to obtain habitat use information across the species’ range to better understand anthropogenic threats, e.g., marine development and climate change. The objective of our study was to investigate the winter movement patterns and habitat use of Black Scoters in the Atlantic Ocean by quantifying the following key movement indices: number of wintering sites, arrival and departure dates to and from the wintering grounds, days at a wintering site, area of a wintering site, distance between wintering sites, and differences in habitat features of wintering sites. We also tested if winter movement patterns varied by sex or along a latitudinal gradient. To quantify winter movement patterns of Black Scoters, we used satellite telemetry data from 2009 to 2012 (n = 29 tagged females and 15 males for a total of 66 winter seasons, 38 female winter seasons, 28 male winter seasons). Our results indicated that the average wintering site area and distance between wintering sites varied with latitude. Wintering sites located at southern latitudes were larger and further apart than wintering sites located at more northern latitudes. Additionally, wintering sites varied in bathymetry, distance to shore, and the slope of the ocean floor at different latitudes; northern wintering sites were in deeper waters, closer to shore, and on steeper slopes than southern wintering sites. Our results suggest that habitat use may differ by latitude, indicating that habitats used in northern locations may not be representative of habitats used in more southern wintering areas. Understanding variation of habitat use along a latitudinal gradient will enable managers to focus sampling effort for Black Scoter abundance and distribution along the Atlantic coast and provide insight on the wintering ecology and movement of Black Scoters.</span></p>","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ACE-01654-150206","usgsCitation":"Plumpton, H.M., Gilliland, S.G., and Ross, B., 2020, Movement ecology and habitat use differences in Black Scoters wintering along the Atlantic coast: Avian Conservation and Ecology, v. 15, no. 2, 6, https://doi.org/10.5751/ACE-01654-150206.","productDescription":"6","ipdsId":"IP-101476","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01654-150206","text":"Publisher Index Page"},{"id":388234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Florida, Georgia, Maryland, Massachusetts, New Jersey, New York, North Carolina, Rhode Island, South Carolina, Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.33203125,\n              28.844673680771795\n            ],\n            [\n              -80.4638671875,\n              31.316101383495624\n            ],\n            [\n              -75.4541015625,\n              34.19817309627726\n            ],\n            [\n              -74.5751953125,\n              35.85343961959182\n            ],\n            [\n              -74.794921875,\n              37.54457732085582\n            ],\n            [\n              -73.916015625,\n              39.027718840211605\n            ],\n            [\n              -69.3017578125,\n              41.343824581185686\n            ],\n            [\n              -70.7080078125,\n              42.032974332441405\n            ],\n            [\n              -72.7734375,\n              41.47566020027821\n            ],\n            [\n              -74.3994140625,\n              40.97989806962013\n            ],\n            [\n              -76.728515625,\n              39.36827914916014\n            ],\n            [\n              -77.34374999999999,\n              37.92686760148135\n            ],\n            [\n              -76.46484375,\n              36.527294814546245\n            ],\n            [\n              -76.81640625,\n              35.60371874069731\n            ],\n            [\n              -77.255859375,\n              34.88593094075317\n            ],\n            [\n              -79.2333984375,\n              33.7243396617476\n            ],\n            [\n              -81.123046875,\n              32.13840869677249\n            ],\n            [\n              -81.5625,\n              30.939924331023445\n            ],\n            [\n              -81.0791015625,\n              28.8831596093235\n            ],\n            [\n              -80.33203125,\n              28.844673680771795\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"15","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Plumpton, H. 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